Host–gut microbiota interactions shape parasite infections in farmed Atlantic salmon

ABSTRACT Animals and their associated microbiota share long evolutionary histories. However, it is not always clear how host genotype and microbiota interact to affect phenotype. We applied a hologenomic approach to explore how host–microbiota interactions shape lifetime growth and parasite infection in farmed Atlantic salmon (Salmo salar). Multi-omics data sets were generated from the guts of 460 salmon, 82% of which were naturally infected with an intestinal cestode. A single Mycoplasma bacterial strain, MAG01, dominated the gut metagenome of large, non-parasitized fish, consistent with previous studies showing high levels of Mycoplasma in the gut microbiota of healthy salmon. While small and/or parasitized salmon also had high abundance of MAG01, we observed increased alpha diversity in these individuals, driven by increased frequency of low-abundance Vibrionaceae and other Mycoplasma species that carried known virulence genes. Colonization by one of these cestode-associated Mycoplasma strains was associated with host individual genomic variation in long non-coding RNAs. Integrating the multi-omic data sets revealed coordinated changes in the salmon gut mRNA transcriptome and metabolome that correlated with shifts in the microbiota of smaller, parasitized fish. Our results suggest that the gut microbiota of small and/or parasitized fish is in a state of dysbiosis that partly depends on the host genotype, highlighting the value of using a hologenomic approach to incorporate the microbiota into the study of host–parasite dynamics. IMPORTANCE Studying host–microbiota interactions through the perspective of the hologenome is gaining interest across all life sciences. Intestinal parasite infections are a huge burden on human and animal health; however, there are few studies investigating the role of the hologenome during parasite infections. We address this gap in the largest multi-omics fish microbiota study to date using natural cestode infection of farmed Atlantic salmon. We find a clear association between cestode infection, salmon lifetime growth, and perturbation of the salmon gut microbiota. Furthermore, we provide the first evidence that the genetic background of the host may partly determine how the gut microbiota changes during parasite-associated dysbiosis. Our study therefore highlights the value of a hologenomic approach for gaining a more in-depth understanding of parasitism.

Detailed results from the PERMANOVAs are presented in Table S1.PERMANOVAs were based on Euclidean distances.S1.S5.  4).The factor numbers are not comparable between models (for links between factors in each model, see Table S9).( A   (C) Ladderlectin and (D) apolipoprotein A-I (APOA1), two genes involved in immune-related pathways that were associated with small fish and Vibrionaceae MAGs in the MOFA (factor 7 in Fig. 4).(E) Actin, alpha skeletal muscle 2 (Actin), a gene involved in cell division and the cytoskeleton that was associated with large fish in the MOFA (factor 7 in Fig. 4).(F) Ankyrin repeat and KH domain-containing protein 1-like (ANKHD1-like), which contained a SNP that was associated with MAG05 detection in the GWAS (Fig. 3).

Fig. S2 .
Fig. S2.Ordination (PCA) of host gut epithelial transcriptomes.Individuals showed some variation by feed type on PC3 (A) and some variation by size class on PC4 (B).PCAs were based on normalised and transformed gene expression data.Based on PERMANOVA results using Euclidean distances, feed type contributed to 2.7% of the variation in gene expression values (F = 9.63, p = 0.001) and size class contributed to 2.0% of the variation (F = 3.53, p = 0.001).Detailed results from the PERMANOVAs are presented in TableS1.

Fig. S3 .
Fig. S3.Ordination (PCA) of gut metabolomes, before (A, C, E) and after (B, D, F) removal of batch effects.Batch effects are a common problem in metabolomic studies and in this study, metabolome batch explains 11.4% of the variation in metabolite abundance before correction

Fig. S4 .
Fig. S4.Ordination (PCA) of fatty acid profiles in salmon muscle.Individuals show strong variation by feed type on PC1 and some variation by fatty acid processing batch on PC2 (A) and some variation by size class on PC3 (B).PCAs were based on normalised and transformed fatty acid abundances.Based on PERMANOVA results using Euclidean distances, feed type contributed to 34.8% of the variation in fatty acid abundances (F = 242.4,p = 0.001), size class contributed to 5.8% of the variation (F = 20.33,p = 0.001) and fatty acid processing batch contributed to 1.0% of the variation (F = 1.82, p = 0.086).Detailed results from the PERMANOVAs are presented in TableS1.

Fig. S6 .
Fig. S6.Comparison of microbial relative abundances in salmon gut content samples captured by MAG assembly versus 16S profiling.The colours represent microbial genera, families, or orders in the case of Alteromonadales, as defined in the legend.The dashed line indicates hypothetical perfect concordance in relative abundances between the MAG and 16S profiling methods.Relative abundances are shown on a log10 scale.Generally, good agreement was observed for the most common microbial taxa.However, Psychromonas and Clostridium were only detected by MAG assembly (with relative abundances < 0.1%), while Neiella was only detected by 16S profiling.Psychromonas and Neiella are both of the order Alteromonadales and have somewhat similar relative abundances in the MAG and 16S datasets, respectively,suggesting that they may partly represent the same cluster of microbes that were assigned different taxonomies at lower levels due to differences in the databases used.'Other' represents all 16S amplicon sequencing variants (ASVs) that are not represented by the MAG catalogue and accounts for approximately 0.1% of the total number of reads assigned to ASVs.

Fig. S7 .
Fig. S7.Microbiota community composition of salmon gut content (same sample type as used for MAG analysis) compared to salmon gut mucosa scrapes and feed pellet, determined by 16S metabarcoding.(A) Heatmap of the abundance (centre-log-ratio normalised) of the 64 amplicon sequence variants (ASVs) present in at least 5 samples after quality control.ASVs are annotated by their taxonomic clade, where "other" indicates ASVs that could not be

Fig. S8 .
Fig. S8.Manhattan plots of all GWAS runs.P-values of individual SNPs have been transformed on a -log10 axis.Red lines indicate the threshold for genome-wide significance (p < 5e-8) and blue lines indicate the suggestive threshold (p < 1e-5).Results of GWAS are summarised in TableS5.

Fig. S9 .
Fig. S9.MOFA results from the feed1 model, for factors 1, 2, 3, 6 and 7.These factors from the feed1 model are shown here because they captured similar patterns in variation as in the full combined model (Fig. 4).The factor numbers are not comparable between models (for ) Factors 1, 6 and 7 were correlated with size class.(B) Factors 3 and 6 were correlated with cestode detection.(C-E) Feature weights for the metagenome (C), metabolome (D) and transcriptome (E) for factors 1, 2, 3, 6 and 7. Features are ranked according to their weight.The higher the absolute weight, the more strongly associated a feature is with that factor.A positive weight indicates the feature has higher levels in samples with positive factor values, while a negative weight indicates the opposite.Features with weights > 0.2 or < -0.2 are coloured by MAG species or genus (C) or most frequent functional annotation (D-E), while features with less frequent annotations, or those with weights outside the threshold range (as indicated by the dashed lines) are shown in grey.

Fig. S10 .
Fig. S10.MOFA results from the feed2 model, for factors 1, 2, 4-6.These factors from the feed2 model are shown here because they captured similar patterns in variation as in the full combined model (Fig. 4).The factor numbers are not comparable between models (for links between factors in each model, see Table S9).(A) Factors 1, 5 and 6 were correlated with size class.(B) Factors 4 and 6 were correlated with cestode detection.(C-E) Feature weights for the metagenome (C), metabolome (D) and transcriptome (E) for factors 1, 2, 4-6.Features are ranked according to their weight.The higher the absolute weight, the more strongly associated a feature is with that factor.A positive weight indicates the feature has higher levels in samples with positive factor values, while a negative weight indicates the opposite.Features with weights > 0.2 or < -0.2 are coloured by MAG species or genus (C) or most frequent

Fig. S11 .
Fig. S11.RNA coverage curves before and after normalisation by DegNorm for each of the 343 samples included in the final RNA analyses, for six genes of interest, based on the GWAS and MOFA analyses.(A) Glutamate decarboxylase like 1 (GADL1) and (B) cysteine sulfinic acid decarboxylase-like (CSAD-like), two genes involved in taurine biosynthesis that were associated with cestode presence and Mycoplasma MAGs in the MOFA (factor 3 in Fig. 4).